News from the NSF's Temporal Dynamics of Learning Center

April 2007by Garrison W. Cottrell, Ph.D.

A better understanding of the role that timing plays in human learning could lead to improved teaching techniques and alter the trajectories of countless human lives.

Thanks to a $3.5 million National Science Foundation (NSF) grant, with the possibility of an additional $32 million over the next decade, a University of California, San Diego (UCSD)-based interdisciplinary team of scientists, with participation from scientists at Rutgers University, Newark, Vanderbilt University, UC Berkeley, as well as over 10 other institutions and companies are poised to clarify the importance of time and timing in learning.

When you learn the sounds of your language, interact with colleagues and teachers, become proficient at sports or playing a musical instrument, or engage in countless other learning activities, timing plays a critical role in the development of the wiring of your brain cells, in the communication between and within sensory and motor systems, and in the interactions between different regions of your brain. The success or failure of interpersonal communication and social interaction using gestures, facial expressions and verbal language also depend critically on exact timing.

To pick just one common example, giving a student praise for a correct answer will not be as effective if you pause for several seconds before saying "good answer." That delay changes the strength of the feedback, suggesting you are not sure of it or of the student. One of the Center's goals is to understand the mechanisms of reward in the brain, and how timing makes a difference.

I am joined in this effort by over 40 researchers from across the United States, Canada, and Australia. We are a highly interdisciplinary group. I am a cognitive scientist in the Computer Science and Engineering Department at UCSD. My co-Director Andrea Chiba is a behavioral neuroscientist in the Cognitive Science Department at UCSD. My other co-Director is Terry Sejnowski, who was initially trained as a biophysicist, and now does neuroscience and computational modeling at the Salk Institute in La Jolla. We are joined by psychologists, neuropsychologists, neuroscientists, physicists, computational modelers - the list goes on and on!

What brings us all together is a realization of the important role of time in learning. We are studying everything from "spike-time dependent plasticity" - our current best guess at how neurons learn - to the timing of social interactions between teachers and students. In order to organize such a diverse group of scientists, we have gathered ourselves together into four research networks of about ten scientists each. Each research network is focusing on a different aspect of how we learn. For example, it may not be obvious, but we learn to see and hear. Our brains begin to organize the "blooming buzzing confusion" around us even in the womb. The Sensorimotor Network, led by neurobiologist Dan Feldman at UCSD, studies how sensory systems learn, as well as how we learn to control our bodies (the "motor system").

The Interacting Memory Systems Network, led by Andrea Chiba, investigates how different brain systems work together to form memories. One issue they are studying is how the time between study sessions affects performance on tests. Another project is investigating how the new brain cells that are born every day in a specific portion of our brain are used in forming new memories.

The Perceptual Expertise Network, led by Isabel Gauthier and Tom Palmeri of Vanderbilt University, is studying how we become experts at visual discrimination tasks. For example, we are all "face experts" - we recognize hundreds of different people as separate individuals, even though in some sense, we all look very similar. Imagine trying to do the same with dogs - a dog show judge can do this, but most of us cannot. How is this kind of skill acquired? What are the best ways to teach such skills, especially to children with autism, who typically don't pay much attention to faces?

The Social Interaction Network, led by Javier Movellan, a cognitive scientist at the Institute for Neural Computation at UCSD, is studying how teachers and students interact in order to achieve good educational outcomes. His group has implemented their ideas in a social robot, RUBI, who teaches toddlers their colors and shapes according to the California standards for preschoolers. They have found that the timing of RUBI's reactions to the children are crucial in getting them to accept RUBI as a social being rather than a machine.

A very important component of our Center is the Education and Outreach component that is directed by Paula Tallal from Rutgers University and Terry Sejnowski. Their goal is to decrease the time between scientific discovery and dissemination to classrooms. Both Scientific Learning Corporation and Jensen Learning Corporation will participate in the TDLC as corporate partners.

One of the ongoing Education and Outreach projects of the TDLC will be to contribute a quarterly column to BrainConnection.com that will highlight new research advances coming from the TDLC that are of most relevance to K-12 educators and students.

The Vision of the Temporal Dynamics of Learning Center (TDLC)

The Temporal Dynamics of Learning Center aims to achieve an integrated understanding of the role of time and timing in learning, across multiple scales, brain systems, and social systems. The scientific goal of the center is, therefore, to understand the temporal dynamics of learning, and to apply this understanding to improve educational practice.

Learning is an active, dynamic behavior that emerges from interactions between the developing brain of a child and a social world. Until recently not enough was known about the brain to help guide educational practice. This is rapidly changing as new discoveries are made about the brain and new techniques are available for probing the learning brain. TDLC brings together a collaborative team of researchers, educators, and communicators who bring basic science into classrooms and, conversely, use the classroom as a living laboratory to inform and guide the basic science.

Learning occurs at many levels: at the level of synapses and neurons; at the level of brain systems involved in memory and reward; at the level of complex motor behaviors; at the level of expertise learning; and finally, at the level of learning via social interactions between teachers and students. TDLC initiatives address fundamental research questions such as:

How is temporal information about the world learned? How do the intrinsic temporal dynamic properties of brain cells and circuits facilitate and/or constrain learning? How can the temporal features of learning be used to enhance education?

What are the best theoretical ways to conceive the temporal dynamics of learning in the brain and between brains?

Answering these questions cannot emerge from a single line of inquiry, so TDLC's research model has been collaborative and interdisciplinary from the beginning. The center has created communities of scientists that break down disciplinary and institutional barriers in pursuit of a common set of research questions. Researchers in machine learning, psychology, cognitive science, neuroscience, molecular genetics, biophysics, mathematics, and education focus on each set of issues from multiple perspectives, and synchronize their research by running parallel experiments in animals, people, and theoretical models.

TDLC researchers hope to improve teacher understanding of the scientific research pertaining to the dynamics of learning. They also learn from teachers the dynamics of how students are taught in the classroom. Through this process of outreach and bringing teachers into the laboratory, the center's researchers hope to ensure that their work will be relevant to the real world of the classroom.

Why Time Matters

Time and timing is critical for learning at every level, from learning the precise temporal patterns of speech sounds, to learning appropriate sequences of movements, to optimal training and instructional schedules for learning, to interpreting the streams of social signals that reinforce learning in the classroom.

Learning depends on the fine-scale structure of the timing between stimuli, response, and reward. The brain is exquisitely sensitive to the temporal structure of sensory experience:

at the millisecond time scale in the auditory system;at the second time scale in reinforcement learning;at the minute time scale for action-perception adaptation; andat the day-to-week time scale for consolidation and maturation.

Each level of learning has its own temporal dynamics, and its own timing constraints that affect learning. These levels are not independent, but instead, timing constraints at one level affect learning at another level in a nested way. For example, the dynamics at the cellular level, which is often on the order of milliseconds, implement learning on the whole-brain and behavioral level on much longer time scales, including memories that last a lifetime.

The past decade of neuroscience research demonstrates that the intrinsic temporal dynamics of processes within the brain also reinforce and constrain learning. For example, we have discovered that slow learners tend to have slow "shutter speeds" in terms of how their brains take in and process information. For some poor readers, the underlying problem is their inability to perceive fast acoustic changes in speech sounds (phonemes) that must be accurately perceived in order to learn letter-sound correspondence rules for reading. Neuroscience-based training regimes that improve this temporal processing ability (Fast ForWord) improve both spoken and written language learning in struggling readers.

Our hope is that by understanding how time and timing affects learning at multiple levels, we will be able to understand and optimize learning in the classroom. This is an ambitious goal, and we are just at the beginning of our quest. We are motivated to keep our research relevant to education by engaging with teachers at all stages of our work. We are excited by the possibilities, and we hope you will also be excited by our discoveries in the years to come.

Garrison W. Cottrell is a Professor of Computer Science and Engineering at UC San Diego. Professor Cottrell's main interest is Cognitive Science, in particular, building working models of cognitive processes and using them to explain psychological or neurological processes. In recent years, he has focused upon face processing, including face recognition, face identification, and facial expression recognition. He has also worked in the areas of modeling psycholinguistic processes, such as language acquisition, reading, and word sense disambiguation. He received his PhD. in 1985 from the University of Rochester under James F. Allen (thesis title: A connectionist approach to word sense disambiguation). He then did a post doc with David E. Rumelhart at the Institute of Cognitive Science, UCSD, until 1987, when he joined the CSE Department.